{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scaling to quantiles and median - RobustScaling\n",
    "\n",
    "In this procedure the median is removed from the observations and then they are scaled to the inter-quantile range (IQR). The IQR is the range between the 1st quartile (25th quantile) and the 3rd quartile (75th quantile).\n",
    "\n",
    "X_scaled = X - X_median / ( X.quantile(0.75) - X.quantile(0.25) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.datasets import fetch_california_housing\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# the scaler - for robust scaling\n",
    "from sklearn.preprocessing import RobustScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup\n",
       "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556\n",
       "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842\n",
       "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260\n",
       "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945\n",
       "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the California House price data from Scikit-learn\n",
    "X, y = fetch_california_housing(return_X_y=True, as_frame=True)\n",
    "\n",
    "# Remove 2 variables:\n",
    "X.drop(labels=[\"Latitude\", \"Longitude\"], axis=1, inplace=True)\n",
    "\n",
    "# display top 5 rows\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((14448, 6), (6192, 6))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's separate the data into training and testing sets\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X,\n",
    "    y,\n",
    "    test_size=0.3,\n",
    "    random_state=0,\n",
    ")\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up the scaler\n",
    "scaler = RobustScaler().set_output(transform=\"pandas\")\n",
    "\n",
    "# fit the scaler to the train set, it will learn the parameters\n",
    "scaler.fit(X_train)\n",
    "\n",
    "# transform train and test sets\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.53910000e+00, 2.90000000e+01, 5.22931763e+00, 1.04878049e+00,\n",
       "       1.16500000e+03, 2.81635506e+00])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the scaler stores the median values of the features as learned from train set\n",
    "\n",
    "scaler.center_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.16550000e+00, 1.90000000e+01, 1.59537022e+00, 9.41284380e-02,\n",
       "       9.40000000e+02, 8.53176853e-01])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the scaler stores the IQR values of the features as learned from train set\n",
    "\n",
    "scaler.scale_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.880013</td>\n",
       "      <td>28.687984</td>\n",
       "      <td>5.442057</td>\n",
       "      <td>1.101109</td>\n",
       "      <td>1426.222061</td>\n",
       "      <td>3.140976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.920007</td>\n",
       "      <td>12.560416</td>\n",
       "      <td>2.862733</td>\n",
       "      <td>0.519956</td>\n",
       "      <td>1091.567168</td>\n",
       "      <td>15.796292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.499900</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.465753</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.552150</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>4.414452</td>\n",
       "      <td>1.006494</td>\n",
       "      <td>796.000000</td>\n",
       "      <td>2.436452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.529600</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.227365</td>\n",
       "      <td>1.048741</td>\n",
       "      <td>1169.500000</td>\n",
       "      <td>2.825041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.768750</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>6.064257</td>\n",
       "      <td>1.098434</td>\n",
       "      <td>1727.250000</td>\n",
       "      <td>3.285501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.000100</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>141.909091</td>\n",
       "      <td>25.636364</td>\n",
       "      <td>16305.000000</td>\n",
       "      <td>1243.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            MedInc     HouseAge     AveRooms    AveBedrms    Population  \\\n",
       "count  6192.000000  6192.000000  6192.000000  6192.000000   6192.000000   \n",
       "mean      3.880013    28.687984     5.442057     1.101109   1426.222061   \n",
       "std       1.920007    12.560416     2.862733     0.519956   1091.567168   \n",
       "min       0.499900     1.000000     1.465753     0.500000      8.000000   \n",
       "25%       2.552150    18.000000     4.414452     1.006494    796.000000   \n",
       "50%       3.529600    29.000000     5.227365     1.048741   1169.500000   \n",
       "75%       4.768750    37.000000     6.064257     1.098434   1727.250000   \n",
       "max      15.000100    52.000000   141.909091    25.636364  16305.000000   \n",
       "\n",
       "          AveOccup  \n",
       "count  6192.000000  \n",
       "mean      3.140976  \n",
       "std      15.796292  \n",
       "min       0.692308  \n",
       "25%       2.436452  \n",
       "50%       2.825041  \n",
       "75%       3.285501  \n",
       "max    1243.333333  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspect the original value statistics\n",
    "X_test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.157429</td>\n",
       "      <td>-0.016422</td>\n",
       "      <td>0.133348</td>\n",
       "      <td>0.555929</td>\n",
       "      <td>0.277896</td>\n",
       "      <td>0.380485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.886634</td>\n",
       "      <td>0.661075</td>\n",
       "      <td>1.794400</td>\n",
       "      <td>5.523897</td>\n",
       "      <td>1.161242</td>\n",
       "      <td>18.514675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.403463</td>\n",
       "      <td>-1.473684</td>\n",
       "      <td>-2.359054</td>\n",
       "      <td>-5.830124</td>\n",
       "      <td>-1.230851</td>\n",
       "      <td>-2.489575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.455761</td>\n",
       "      <td>-0.578947</td>\n",
       "      <td>-0.510769</td>\n",
       "      <td>-0.449248</td>\n",
       "      <td>-0.392553</td>\n",
       "      <td>-0.445280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.004387</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.001224</td>\n",
       "      <td>-0.000421</td>\n",
       "      <td>0.004787</td>\n",
       "      <td>0.010181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.567837</td>\n",
       "      <td>0.421053</td>\n",
       "      <td>0.523352</td>\n",
       "      <td>0.527508</td>\n",
       "      <td>0.598138</td>\n",
       "      <td>0.549881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.292542</td>\n",
       "      <td>1.210526</td>\n",
       "      <td>85.672762</td>\n",
       "      <td>261.213122</td>\n",
       "      <td>16.106383</td>\n",
       "      <td>1453.997462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            MedInc     HouseAge     AveRooms    AveBedrms   Population  \\\n",
       "count  6192.000000  6192.000000  6192.000000  6192.000000  6192.000000   \n",
       "mean      0.157429    -0.016422     0.133348     0.555929     0.277896   \n",
       "std       0.886634     0.661075     1.794400     5.523897     1.161242   \n",
       "min      -1.403463    -1.473684    -2.359054    -5.830124    -1.230851   \n",
       "25%      -0.455761    -0.578947    -0.510769    -0.449248    -0.392553   \n",
       "50%      -0.004387     0.000000    -0.001224    -0.000421     0.004787   \n",
       "75%       0.567837     0.421053     0.523352     0.527508     0.598138   \n",
       "max       5.292542     1.210526    85.672762   261.213122    16.106383   \n",
       "\n",
       "          AveOccup  \n",
       "count  6192.000000  \n",
       "mean      0.380485  \n",
       "std      18.514675  \n",
       "min      -2.489575  \n",
       "25%      -0.445280  \n",
       "50%       0.010181  \n",
       "75%       0.549881  \n",
       "max    1453.997462  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect the values after scaling\n",
    "\n",
    "X_test_scaled.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_test.hist(bins=20, figsize=(20, 12), layout=(2, 3))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_test_scaled.hist(bins=20, figsize=(20, 12), layout=(2, 3))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fsml",
   "language": "python",
   "name": "fsml"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": "block",
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
